DATA VISUALIZATION IN CLINICAL DATA MANAGEMENT (CDM)

Kombathula Jayasheela*, Shaikh Sameer and Sruthi

ABSTRACT

Data visualization techniques can assist Healthcare providers in understanding large sets of clinical data, Data visualization in the context of clinical data management refers to the graphical representation of clinical data to facilitate analysis, interpretation, and communication of information related to healthcare and patient outcomes. data visualization in clinical data management transforms complex clinical data into visual representations that simplify analysis, support decision-making, and enhance communication. It enhances data understanding, aids in pattern identification and insights, supports efficient exploration, facilitates communication and collaboration, assists in decision-making, ensures data quality, and improves presentation and reporting. The purpose of data visualization in clinical data management is to effectively communicate and understand complex clinical data through visual representations. Enhancing data understanding and gaining valuable insights through data visualization in clinical data management is crucial. Facilitating the identification of patterns, trends, and outliers through data visualization is of great importance in clinical data management. The right visual aid is the key to preventing user confusion and making sure our analysis should be accurate. To design visualizations for solving real-world problems, we typically conduct design studies. In a design study, we engage closely with domain experts and their data analysis problems. The very first step of a design study is the domain characterization. Domain characterization describes domain-specific problems, such as what data is needed. A graphical depiction of data and information is called data visualisation. Data visualisation tools can offer a simple way to anticipate and comprehend trends and patterns in data by employing visual elements like graphs, charts, and maps. They assist us in categorising and organising data based on categories and themes, which makes it simpler to digest and comprehend. Using visualisation tools like CDMS, EDC, Epro, etc. Addressing data complexity and visualization, ensuring interpretability and accuracy considerations in data visualization, clinical data management systems can ensure that visualizations are reliable, trustworthy, and facilitate accurate insights.

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